Overview

Dataset statistics

Number of variables12
Number of observations243596
Missing cells0
Missing cells (%)0.0%
Duplicate rows5421
Duplicate rows (%)2.2%
Total size in memory22.3 MiB
Average record size in memory96.0 B

Variable types

Numeric9
Categorical3

Alerts

Dataset has 5421 (2.2%) duplicate rowsDuplicates
fps_lags is highly correlated with dropped_frames_mean and 2 other fieldsHigh correlation
dropped_frames_mean is highly correlated with fps_lags and 2 other fieldsHigh correlation
dropped_frames_std is highly correlated with fps_lags and 2 other fieldsHigh correlation
dropped_frames_max is highly correlated with fps_lags and 2 other fieldsHigh correlation
rtt_mean is highly correlated with rtt_stdHigh correlation
rtt_std is highly correlated with rtt_meanHigh correlation
dropped_frames_mean is highly correlated with dropped_frames_maxHigh correlation
dropped_frames_max is highly correlated with dropped_frames_meanHigh correlation
fps_lags is highly correlated with dropped_frames_mean and 2 other fieldsHigh correlation
dropped_frames_mean is highly correlated with fps_lags and 2 other fieldsHigh correlation
dropped_frames_std is highly correlated with fps_lags and 2 other fieldsHigh correlation
dropped_frames_max is highly correlated with fps_lags and 2 other fieldsHigh correlation
fps_mean is highly correlated with fps_std and 1 other fieldsHigh correlation
fps_std is highly correlated with fps_meanHigh correlation
fps_lags is highly correlated with fps_mean and 1 other fieldsHigh correlation
rtt_mean is highly correlated with rtt_stdHigh correlation
rtt_std is highly correlated with rtt_meanHigh correlation
dropped_frames_mean is highly correlated with dropped_frames_maxHigh correlation
dropped_frames_max is highly correlated with dropped_frames_meanHigh correlation
auto_fec_state is highly correlated with auto_fec_meanHigh correlation
auto_fec_mean is highly correlated with auto_fec_stateHigh correlation
stream_quality is highly correlated with fps_lagsHigh correlation
rtt_mean is highly skewed (γ1 = 33.39349025) Skewed
dropped_frames_mean is highly skewed (γ1 = 186.5391544) Skewed
dropped_frames_std is highly skewed (γ1 = 486.3128689) Skewed
dropped_frames_max is highly skewed (γ1 = 186.5391543) Skewed
fps_std has 58199 (23.9%) zeros Zeros
fps_lags has 228145 (93.7%) zeros Zeros
rtt_mean has 3514 (1.4%) zeros Zeros
rtt_std has 9344 (3.8%) zeros Zeros
dropped_frames_mean has 224288 (92.1%) zeros Zeros
dropped_frames_std has 224703 (92.2%) zeros Zeros
dropped_frames_max has 224288 (92.1%) zeros Zeros
auto_fec_mean has 36416 (14.9%) zeros Zeros

Reproduction

Analysis started2022-10-05 01:41:22.177105
Analysis finished2022-10-05 01:41:33.840362
Duration11.66 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

fps_mean
Real number (ℝ≥0)

HIGH CORRELATION

Distinct639
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.65525173
Minimum0
Maximum84.6
Zeros370
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2022-10-05T04:41:34.029830image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23.8
Q128.6
median30
Q342.5
95-th percentile57.5
Maximum84.6
Range84.6
Interquartile range (IQR)13.9

Descriptive statistics

Standard deviation10.98654749
Coefficient of variation (CV)0.3170240278
Kurtosis-0.06269964055
Mean34.65525173
Median Absolute Deviation (MAD)2.8
Skewness0.8680873114
Sum8441880.7
Variance120.7042257
MonotonicityNot monotonic
2022-10-05T04:41:34.110833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3053058
 
21.8%
29.99793
 
4.0%
29.85662
 
2.3%
29.74144
 
1.7%
30.13939
 
1.6%
29.63309
 
1.4%
29.52808
 
1.2%
29.42345
 
1.0%
292086
 
0.9%
29.32067
 
0.8%
Other values (629)154385
63.4%
ValueCountFrequency (%)
0370
0.2%
0.116
 
< 0.1%
0.27
 
< 0.1%
0.37
 
< 0.1%
0.44
 
< 0.1%
0.52
 
< 0.1%
0.61
 
< 0.1%
0.74
 
< 0.1%
0.84
 
< 0.1%
0.95
 
< 0.1%
ValueCountFrequency (%)
84.61
< 0.1%
78.61
< 0.1%
77.11
< 0.1%
75.81
< 0.1%
74.11
< 0.1%
72.51
< 0.1%
71.41
< 0.1%
71.21
< 0.1%
651
< 0.1%
64.71
< 0.1%

fps_std
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct18713
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.261398073
Minimum0
Maximum150.7660439
Zeros58199
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2022-10-05T04:41:34.189850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.316227766
median0.994428926
Q32.547329757
95-th percentile9.501461876
Maximum150.7660439
Range150.7660439
Interquartile range (IQR)2.231101991

Descriptive statistics

Standard deviation3.554645289
Coefficient of variation (CV)1.571879508
Kurtosis48.31494126
Mean2.261398073
Median Absolute Deviation (MAD)0.994428926
Skewness3.953706957
Sum550867.5249
Variance12.63550313
MonotonicityNot monotonic
2022-10-05T04:41:34.270927image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
058199
 
23.9%
0.3162277663762
 
1.5%
0.3162277662858
 
1.2%
0.3162277662556
 
1.0%
0.3162277662525
 
1.0%
0.3162277661793
 
0.7%
0.47140452081280
 
0.5%
0.94868329811252
 
0.5%
0.6324555321179
 
0.5%
0.42163702141154
 
0.5%
Other values (18703)167038
68.6%
ValueCountFrequency (%)
058199
23.9%
0.316227766123
 
0.1%
0.3162277662858
 
1.2%
0.3162277661793
 
0.7%
0.3162277662525
 
1.0%
0.3162277663762
 
1.5%
0.3162277662556
 
1.0%
0.316227766198
 
0.1%
0.316227766115
 
< 0.1%
0.316227766121
 
< 0.1%
ValueCountFrequency (%)
150.76604391
< 0.1%
150.2333371
< 0.1%
100.79577811
< 0.1%
96.068494081
< 0.1%
90.893405211
< 0.1%
90.089400041
< 0.1%
87.348853581
< 0.1%
77.429896611
< 0.1%
72.601576351
< 0.1%
69.350078111
< 0.1%

fps_lags
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.103252927
Minimum0
Maximum10
Zeros228145
Zeros (%)93.7%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2022-10-05T04:41:34.342720image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.6158780258
Coefficient of variation (CV)5.964751255
Kurtosis172.5560032
Mean0.103252927
Median Absolute Deviation (MAD)0
Skewness11.88249546
Sum25152
Variance0.3793057426
MonotonicityNot monotonic
2022-10-05T04:41:34.396734image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0228145
93.7%
112341
 
5.1%
21693
 
0.7%
10558
 
0.2%
3401
 
0.2%
4140
 
0.1%
599
 
< 0.1%
669
 
< 0.1%
766
 
< 0.1%
845
 
< 0.1%
ValueCountFrequency (%)
0228145
93.7%
112341
 
5.1%
21693
 
0.7%
3401
 
0.2%
4140
 
0.1%
599
 
< 0.1%
669
 
< 0.1%
766
 
< 0.1%
845
 
< 0.1%
939
 
< 0.1%
ValueCountFrequency (%)
10558
 
0.2%
939
 
< 0.1%
845
 
< 0.1%
766
 
< 0.1%
669
 
< 0.1%
599
 
< 0.1%
4140
 
0.1%
3401
 
0.2%
21693
 
0.7%
112341
5.1%

rtt_mean
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct5192
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55.27376188
Minimum0
Maximum13456.8
Zeros3514
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2022-10-05T04:41:34.464368image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.8
Q114.6
median30.6
Q359.3
95-th percentile160.8
Maximum13456.8
Range13456.8
Interquartile range (IQR)44.7

Descriptive statistics

Standard deviation163.8729656
Coefficient of variation (CV)2.964751448
Kurtosis1987.056418
Mean55.27376188
Median Absolute Deviation (MAD)19
Skewness33.39349025
Sum13464467.3
Variance26854.34884
MonotonicityNot monotonic
2022-10-05T04:41:34.547669image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03514
 
1.4%
6843
 
0.3%
6.1792
 
0.3%
5790
 
0.3%
6.2771
 
0.3%
16.3731
 
0.3%
5.9722
 
0.3%
16.5710
 
0.3%
6.3710
 
0.3%
6.6702
 
0.3%
Other values (5182)233311
95.8%
ValueCountFrequency (%)
03514
1.4%
0.41
 
< 0.1%
0.53
 
< 0.1%
0.63
 
< 0.1%
0.72
 
< 0.1%
0.82
 
< 0.1%
0.93
 
< 0.1%
15
 
< 0.1%
1.14
 
< 0.1%
1.24
 
< 0.1%
ValueCountFrequency (%)
13456.81
 
< 0.1%
124969
< 0.1%
12058.81
 
< 0.1%
12051.31
 
< 0.1%
11593.61
 
< 0.1%
9008.41
 
< 0.1%
6615.71
 
< 0.1%
6587.71
 
< 0.1%
6584.61
 
< 0.1%
6496.81
 
< 0.1%

rtt_std
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct49646
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.68391714
Minimum0
Maximum9561.738301
Zeros9344
Zeros (%)3.8%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2022-10-05T04:41:34.637487image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.316227766
Q10.7071067812
median1.699673171
Q36.624030327
95-th percentile46.388666
Maximum9561.738301
Range9561.738301
Interquartile range (IQR)5.916923546

Descriptive statistics

Standard deviation146.2428758
Coefficient of variation (CV)7.070366544
Kurtosis432.2814232
Mean20.68391714
Median Absolute Deviation (MAD)1.21662728
Skewness17.43594552
Sum5038519.48
Variance21386.97871
MonotonicityNot monotonic
2022-10-05T04:41:34.722789image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
09344
 
3.8%
0.48304589151965
 
0.8%
0.51639777951846
 
0.8%
0.42163702141698
 
0.7%
0.3162277661632
 
0.7%
0.56764621221574
 
0.6%
0.51639777951538
 
0.6%
0.67494855771412
 
0.6%
0.3162277661300
 
0.5%
0.78881063771250
 
0.5%
Other values (49636)220037
90.3%
ValueCountFrequency (%)
09344
3.8%
0.31622776611
 
< 0.1%
0.31622776611
 
< 0.1%
0.3162277668
 
< 0.1%
0.316227766106
 
< 0.1%
0.316227766392
 
0.2%
0.316227766497
 
0.2%
0.316227766440
 
0.2%
0.316227766150
 
0.1%
0.3162277661632
 
0.7%
ValueCountFrequency (%)
9561.7383011
< 0.1%
7558.193071
< 0.1%
6798.5020911
< 0.1%
6370.9579461
< 0.1%
5771.5524981
< 0.1%
5129.9064151
< 0.1%
5102.5627161
< 0.1%
4855.3900451
< 0.1%
4855.0653271
< 0.1%
4836.8343141
< 0.1%

dropped_frames_mean
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct775
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31538.26549
Minimum0
Maximum1097304300
Zeros224288
Zeros (%)92.1%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2022-10-05T04:41:34.804853image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.6
Maximum1097304300
Range1097304300
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5882144.321
Coefficient of variation (CV)186.5081744
Kurtosis34795.14214
Mean31538.26549
Median Absolute Deviation (MAD)0
Skewness186.5391544
Sum7682595320
Variance3.459962181 × 1013
MonotonicityNot monotonic
2022-10-05T04:41:34.884635image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0224288
92.1%
3.4634
 
0.3%
3.3616
 
0.3%
3.5489
 
0.2%
3.2353
 
0.1%
3.6342
 
0.1%
6.5299
 
0.1%
3.7289
 
0.1%
6286
 
0.1%
5.9284
 
0.1%
Other values (765)15716
 
6.5%
ValueCountFrequency (%)
0224288
92.1%
0.1204
 
0.1%
0.276
 
< 0.1%
0.376
 
< 0.1%
0.480
 
< 0.1%
0.574
 
< 0.1%
0.691
 
< 0.1%
0.787
 
< 0.1%
0.877
 
< 0.1%
0.975
 
< 0.1%
ValueCountFrequency (%)
10973043007
 
< 0.1%
6553019
< 0.1%
327651
 
< 0.1%
2121
 
< 0.1%
198.11
 
< 0.1%
196.11
 
< 0.1%
195.51
 
< 0.1%
192.91
 
< 0.1%
191.11
 
< 0.1%
190.31
 
< 0.1%

dropped_frames_std
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct4686
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.573090527
Minimum0
Maximum34537.34251
Zeros224703
Zeros (%)92.2%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2022-10-05T04:41:34.966819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile12.96533841
Maximum34537.34251
Range34537.34251
Interquartile range (IQR)0

Descriptive statistics

Standard deviation70.32015208
Coefficient of variation (CV)44.70191057
Kurtosis238832.9733
Mean1.573090527
Median Absolute Deviation (MAD)0
Skewness486.3128689
Sum383198.5601
Variance4944.923788
MonotonicityNot monotonic
2022-10-05T04:41:35.044211image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0224703
92.2%
10.43551628608
 
0.2%
11.06797181471
 
0.2%
10.75174404418
 
0.2%
10.11928851350
 
0.1%
11.38419958329
 
0.1%
11.70042734284
 
0.1%
18.65743819253
 
0.1%
18.97366596243
 
0.1%
12.01665511226
 
0.1%
Other values (4676)15711
 
6.4%
ValueCountFrequency (%)
0224703
92.2%
0.316227766207
 
0.1%
0.3162277661
 
< 0.1%
0.42163702148
 
< 0.1%
0.42163702141
 
< 0.1%
0.48304589154
 
< 0.1%
0.51639777955
 
< 0.1%
0.52704627672
 
< 0.1%
0.6324555321
 
< 0.1%
0.63245553269
 
< 0.1%
ValueCountFrequency (%)
34537.342511
< 0.1%
422.581341
< 0.1%
389.81261881
< 0.1%
355.79521261
< 0.1%
335.44338821
< 0.1%
296.42731921
< 0.1%
291.8782281
< 0.1%
272.40035891
< 0.1%
260.3034341
< 0.1%
254.95803581
< 0.1%

dropped_frames_max
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct372
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31541.88123
Minimum0
Maximum1097304300
Zeros224288
Zeros (%)92.1%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2022-10-05T04:41:35.128986image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile38
Maximum1097304300
Range1097304300
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5882144.302
Coefficient of variation (CV)186.4867939
Kurtosis34795.14211
Mean31541.88123
Median Absolute Deviation (MAD)0
Skewness186.5391543
Sum7683476099
Variance3.459962159 × 1013
MonotonicityNot monotonic
2022-10-05T04:41:35.208418image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0224288
92.1%
341024
 
0.4%
33892
 
0.4%
35724
 
0.3%
32484
 
0.2%
60477
 
0.2%
36475
 
0.2%
59451
 
0.2%
37411
 
0.2%
38353
 
0.1%
Other values (362)14017
 
5.8%
ValueCountFrequency (%)
0224288
92.1%
1283
 
0.1%
270
 
< 0.1%
377
 
< 0.1%
479
 
< 0.1%
579
 
< 0.1%
683
 
< 0.1%
786
 
< 0.1%
878
 
< 0.1%
992
 
< 0.1%
ValueCountFrequency (%)
10973043007
 
< 0.1%
6553020
< 0.1%
13431
 
< 0.1%
11311
 
< 0.1%
10791
 
< 0.1%
10341
 
< 0.1%
9231
 
< 0.1%
8541
 
< 0.1%
8411
 
< 0.1%
7572
 
< 0.1%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
off
196985 
full
46365 
partial
 
246

Length

Max length7
Median length3
Mean length3.194375113
Min length3

Characters and Unicode

Total characters778137
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowoff
2nd rowoff
3rd rowoff
4th rowoff
5th rowoff

Common Values

ValueCountFrequency (%)
off196985
80.9%
full46365
 
19.0%
partial246
 
0.1%

Length

2022-10-05T04:41:35.283645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-05T04:41:35.350334image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
off196985
80.9%
full46365
 
19.0%
partial246
 
0.1%

Most occurring characters

ValueCountFrequency (%)
f440335
56.6%
o196985
25.3%
l92976
 
11.9%
u46365
 
6.0%
a492
 
0.1%
p246
 
< 0.1%
r246
 
< 0.1%
t246
 
< 0.1%
i246
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter778137
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f440335
56.6%
o196985
25.3%
l92976
 
11.9%
u46365
 
6.0%
a492
 
0.1%
p246
 
< 0.1%
r246
 
< 0.1%
t246
 
< 0.1%
i246
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin778137
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f440335
56.6%
o196985
25.3%
l92976
 
11.9%
u46365
 
6.0%
a492
 
0.1%
p246
 
< 0.1%
r246
 
< 0.1%
t246
 
< 0.1%
i246
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII778137
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f440335
56.6%
o196985
25.3%
l92976
 
11.9%
u46365
 
6.0%
a492
 
0.1%
p246
 
< 0.1%
r246
 
< 0.1%
t246
 
< 0.1%
i246
 
< 0.1%

auto_fec_state
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
partial
207180 
off
36416 

Length

Max length7
Median length7
Mean length6.402026306
Min length3

Characters and Unicode

Total characters1559508
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpartial
2nd rowpartial
3rd rowpartial
4th rowpartial
5th rowpartial

Common Values

ValueCountFrequency (%)
partial207180
85.1%
off36416
 
14.9%

Length

2022-10-05T04:41:35.411402image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-05T04:41:35.613089image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
partial207180
85.1%
off36416
 
14.9%

Most occurring characters

ValueCountFrequency (%)
a414360
26.6%
p207180
13.3%
r207180
13.3%
t207180
13.3%
i207180
13.3%
l207180
13.3%
f72832
 
4.7%
o36416
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter1559508
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a414360
26.6%
p207180
13.3%
r207180
13.3%
t207180
13.3%
i207180
13.3%
l207180
13.3%
f72832
 
4.7%
o36416
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Latin1559508
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a414360
26.6%
p207180
13.3%
r207180
13.3%
t207180
13.3%
i207180
13.3%
l207180
13.3%
f72832
 
4.7%
o36416
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII1559508
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a414360
26.6%
p207180
13.3%
r207180
13.3%
t207180
13.3%
i207180
13.3%
l207180
13.3%
f72832
 
4.7%
o36416
 
2.3%

auto_fec_mean
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct101
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.4089476
Minimum0
Maximum250
Zeros36416
Zeros (%)14.9%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2022-10-05T04:41:35.669457image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q150
median50
Q350
95-th percentile100
Maximum250
Range250
Interquartile range (IQR)0

Descriptive statistics

Standard deviation35.64902586
Coefficient of variation (CV)0.7215095158
Kurtosis8.447496015
Mean49.4089476
Median Absolute Deviation (MAD)0
Skewness2.2642051
Sum12035822
Variance1270.853045
MonotonicityNot monotonic
2022-10-05T04:41:35.760802image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50180281
74.0%
036416
 
14.9%
10011660
 
4.8%
2008121
 
3.3%
204178
 
1.7%
401316
 
0.5%
60220
 
0.1%
22173
 
0.1%
24111
 
< 0.1%
2679
 
< 0.1%
Other values (91)1041
 
0.4%
ValueCountFrequency (%)
036416
14.9%
42
 
< 0.1%
56
 
< 0.1%
81
 
< 0.1%
1017
 
< 0.1%
122
 
< 0.1%
1514
 
< 0.1%
181
 
< 0.1%
204178
 
1.7%
22173
 
0.1%
ValueCountFrequency (%)
2503
 
< 0.1%
2081
 
< 0.1%
2008121
3.3%
1906
 
< 0.1%
18511
 
< 0.1%
1841
 
< 0.1%
1821
 
< 0.1%
18011
 
< 0.1%
1711
 
< 0.1%
17026
 
< 0.1%

stream_quality
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.9 MiB
0
227902 
1
 
15694

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters243596
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0227902
93.6%
115694
 
6.4%

Length

2022-10-05T04:41:35.845906image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-10-05T04:41:35.917534image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0227902
93.6%
115694
 
6.4%

Most occurring characters

ValueCountFrequency (%)
0227902
93.6%
115694
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number243596
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0227902
93.6%
115694
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Common243596
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0227902
93.6%
115694
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII243596
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0227902
93.6%
115694
 
6.4%

Interactions

2022-10-05T04:41:32.420755image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:25.790287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:26.579179image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:27.356053image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:28.219376image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:29.002481image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:29.804881image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:30.609983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:31.558773image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:32.514802image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:25.882365image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:26.661495image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:27.437227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:28.304056image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:29.084000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:29.889550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:30.822850image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:31.653193image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:32.607323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:25.974578image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:26.749806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:27.519594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:28.392889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:29.176529image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:29.979672image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:30.909975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:31.751314image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:32.697542image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:26.057138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:26.836871image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:27.600786image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:28.478626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:29.263946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:30.069220image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:30.995510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:31.848486image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:32.790577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:26.152279image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:26.924546image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:27.684371image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:28.566392image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:29.353462image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:30.160595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:31.085515image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:31.956204image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:32.882203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:26.242419image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:27.010974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:27.764949image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:28.649422image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:29.440387image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:30.248641image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:31.177435image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:32.051504image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:32.973397image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:26.325491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:27.097682image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:27.847287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:28.739181image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:29.527859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:30.337851image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:31.267619image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:32.143210image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:33.067477image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:26.411592image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:27.187045image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:27.931550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:28.830460image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:29.617664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:30.431341image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:31.364264image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:32.239463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:33.157163image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:26.494227image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:27.273685image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:28.137782image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:28.914892image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:29.715403image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:30.519397image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:31.462857image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-10-05T04:41:32.330720image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-10-05T04:41:35.966120image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-10-05T04:41:36.059272image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-10-05T04:41:36.152636image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-10-05T04:41:36.250339image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-10-05T04:41:36.326732image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-10-05T04:41:33.299433image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-10-05T04:41:33.553788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

fps_meanfps_stdfps_lagsrtt_meanrtt_stddropped_frames_meandropped_frames_stddropped_frames_maxauto_bitrate_stateauto_fec_stateauto_fec_meanstream_quality
029.90.316228055.00.9428090.00.00.0offpartial50.00
130.00.000000055.00.9428090.00.00.0offpartial50.00
230.00.000000056.03.2998320.00.00.0offpartial50.00
329.90.316228058.44.5995170.00.00.0offpartial50.00
430.00.000000054.71.2516660.00.00.0offpartial50.00
530.00.000000054.40.6992060.00.00.0offpartial50.00
629.70.948683055.61.8378730.00.00.0offpartial50.00
730.00.000000058.61.7126980.00.00.0offpartial50.00
830.00.000000057.91.2866840.00.00.0offpartial50.00
930.00.000000057.30.4830460.00.00.0offpartial50.00

Last rows

fps_meanfps_stdfps_lagsrtt_meanrtt_stddropped_frames_meandropped_frames_stddropped_frames_maxauto_bitrate_stateauto_fec_stateauto_fec_meanstream_quality
24358624.00.009.00.00.00.00.0offpartial50.00
24358724.00.009.00.00.00.00.0offpartial50.00
24358824.00.009.00.00.00.00.0offpartial50.00
24358924.00.009.00.00.00.00.0offpartial50.00
24359024.00.009.00.00.00.00.0offpartial50.00
24359124.00.009.00.00.00.00.0offpartial50.00
24359224.00.009.00.00.00.00.0offpartial50.00
24359324.00.009.00.00.00.00.0offpartial50.00
24359424.00.009.00.00.00.00.0offpartial50.00
24359524.00.009.00.00.00.00.0offpartial50.00

Duplicate rows

Most frequently occurring

fps_meanfps_stdfps_lagsrtt_meanrtt_stddropped_frames_meandropped_frames_stddropped_frames_maxauto_bitrate_stateauto_fec_stateauto_fec_meanstream_quality# duplicates
73930.00.000.00.0000000.00.00.0offpartial50.00614
34726.00.000.00.0000000.00.00.0offpartial50.00195
41829.00.000.00.0000000.00.00.0offpartial50.00195
43729.00.00124.00.0000000.00.00.0offpartial50.00170
40128.00.000.00.0000000.00.00.0offpartial50.00130
77930.00.003.30.4830460.00.00.0offpartial50.00105
37727.00.000.00.0000000.00.00.0offpartial50.00102
75130.00.003.00.0000000.00.00.0offpartial50.0083
8224.00.000.00.0000000.00.00.0offpartial50.0078
24025.00.000.00.0000000.00.00.0offpartial50.0070